Lampiran 1. Albedo dari beberapa jenis permukaan Permukaan Tipe Observasi Albedo (%) Pengamat Bay va 3 - 4 KH
Bay and River va 6 - 10 TH
Inland Waters va 5 - 10 L
Ocean va 3 - 7 TH
Ocean, deep va 3 - 5 L
Ocean, near shore, solar elevation 47° tg 4 A Ocean, near shore, solar elevation 43° tg 6 A Ocean, near shore, solar elevation 20° tg 14 A Ocean, near shore, solar elevation 12° tg 30 A
Ocean, near shore, solar elevation 51/2° tg 46 A
Forest Green va 3 - 6 KH
Forest va 4 - 10 TH
Forest va 3 - 5 L
Forest, snow-covered ground va 10 - 25 KH
Ground, bare va 10 - 20 L
Ground, bare, very white va 11 KH Ground, bare, some trees va 7 KH Ground, wet, 70-85% bare ta 8 - 9 F Ground, moist, 70-95% bare ta 9 - 12 F
Black mold, dry tg 14 A
Black mold, wet tg 8 A
Sand, dry tg 18 A
Desert, Mojave ta 24 - 28 M
Desert, Death Valley ta 25 M
Sand, wet tg 9 A
Fields, dry plowed va 20 - 25 TH
Fields, green va 10 - 15 TH
Fields, green va 3 - 6 KH
Fields, wheat va 7 - KH
Fields, unspecified va 5 - 10 L
Grass, dry va 15 - 25 TH
Grass, high dry tg 31 - 33 A
Grass, dry, no sun tg 19 - 22 K
Grass, high fresh tg 26 - A
Grass, high wet tg 22 - A
Grass, wet no sun tg 14 - 26 K
Grass, wet sun tg 33 - 37 K
Snow, fresh tg 81 - A
Snow, several days old, white, smooth tg 70 - 86 A Snow, fresh (highest value) tg 87 - K Snow, old (lowest value) tg 46 - K
Snow, white field va 70 - 86 KH
Ice, sparse snow cover ta 69 - M
Clouds, stratus overcast, 0-500 feet thick ta 5 - 63 N Clouds, stratus overcast, 500-1000 feet thick ta 31 - 75 N
Permukaan Tipe Observasi
Albedo
(%) Pengamat
Clouds, stratus overcast, 1000-2000 feet
thick ta 59 - 84 N
Clouds, dense, opaque va 55 - 78 L
Clouds, dense, nearly opaque va 44 - L
Clouds, thin va 36 - 40 L
Clouds, stratus, 600-1600 feet thick ta 78 - Al Clouds, stratocumulus overcast ta 56 - 81 F Clouds, altostratus, occasional breaks ta 17 - 36 F Clouds, altostratus overcast ta 39 - 59 F Clouds, cirrostratus and altostratus overcast ta 49 - 64 F Clouds, cirrostratus overcast ta 44 - 50 F Keterangan :
Tipe Observasi :
- v : pengukuran albedo dengan menggunakan photometer
- t : pengukuran albedo dengan menggunakan pyrheliometer, pyranometer - a : pengukurang dengan menggunakan aircraft (pesawat)
- g : pengukuran dilakukan di permukaan (ground) Pengamat :
- A : Ångström, A. Geograf. Ann., vol.7, p.321, 1925
- Al : Aldrich, L. B., Smithsonian Misc. Coll., vol.69, No.10, 1919 - B : Baur, F., and philips, H., Gerl. Beitr. Geophys., vol. 42, p.160. - D : Danjon, A., Ann. L’Obs. Strasbourg 3 No.3, p.193, 1936.
- F : Fritz, S. Buil. Amel. Meteorol. Soc., vol. 29. p.303, 1948; vol.31, p.251, 1950; Journ. Meteorol., vol.58, p.59, 1930.
- K : Klitin, N. N., Month. Wheat. Rev., vol.58, p.59, 1930
- KH : Kimball, H. H., and Hand, I. F., Month. Weath. Rev., vol.58, p.280, 1930 - L : Luckiesh, M.Astrophys. Journ., vol.49, p.108, 1919.
- M : MacDonald, T. H., private communication, 1949.
- N : Neiburger, M., U. C. L. A., Dep. Of Meteorol. Papers in Meteorol., No.9, 1948; also Joun. Meteorol., vol.6, p.98, 1949.
Lampiran 2. Parameter Input dan Output dalam model REMO
Parameter Input
129 Surface geopotential (orography)
172 Land sea mask
173 Surface roughness length 229 field capacity of soil 200 leaf area index
226 FAO data set (soil data flags)
212 Vegetation type 198 Vegetation ratio
174 Surface background albedo 199 Orographic variance (for
runoff) 134 Surface pressure 130 Temperature 139 Surface temperature 206 Snow temperature 207 Soil temperature TD3 208 Soil temperature TD4 209 Soil temperature TD5 170 Deep soil temperature 183 Soil temperature 131 u-Velocity 132 v-Velocity
133 Specific humidity 153 Liquid water content 140 Soil wetness
232 Glacier mask
194 Skin reservoir content (t-1) 141 Snow depth 156 Geopotential height ‘********************************** * Parameter Output 130 Temperature 131 u-velocity 132 v-velocity 133 Specific humidity 153 Liquid water content 134 Surface pressure 135 Vertical velocity 139 Surface temperature 140 Soil wetness
141 Snow depth
142 Large scale precipitation 143 Convective precipitation 144 Snow fall
145 Boundary layer dissipation 146 Surface sensible heat flux 147 Surface latent heat flux 159 ustar**3
160 Surface runoff 162 Cloud cover
163 Total cloud cover 164 Total cloud cover 165 10m u-velocity 166 10m v-velocity 167 2m temperature
168 2m dew point temperature 169 Surface temperature 170 Deep soil temperature 171 10m windspeed
172 Land sea mask
173 Surface roughness length 174 Surface background albedo 175 Surface albedo
176 Net surface solar radiation 177 Net surface thermal
radiation
178 Net top solar radiation 179 Top thermal radiation (OLR) 180 Surface u-stress
181 Surface v-stress 182 Surface evaporation 183 Soil temperature
185 Net surf. solar radiation 186 Net surf. thermal radiation 187 Net top solar radiation 188 Net top thermal radiation 189 Surface solar cloud forcing 190 Surface thermal cloud
forcing
191 Top solar cloud forcing 192 Top thermal cloud forcing 194 Skin reservoir content
(t-1)
195 u-Gravity wave stress 196 v-Gravity wave stress 197 Gravity wave dissipation 198 Vegetation ratio
199 Orographic variance (for runoff)
200 Leaf area index
201 Maximum 2m-temperature 202 Minimum 2m-temperature 203 Top solar radiation upward 204 Surface solar radiation
upward
205 Surface thermal radiation upward
206 Snow temperature 207 Soil temperature TD3 208 Soil temperature TD4 209 Soil temperature TD5 210 Sea ice cover
211 Sea ice depth 212 Vegetation type
213 (effective) sea-ice skin temp
214 Maximum surface temperature 215 Minimum surface temperature 216 Maximum 10m-wind speed 217 Maximum heig of conv cloud
top 218 Snow melt
220 Residual surface heat budget
221 Snow depth change 223 Cloud cover 223 Cloud cover
224 Turbulent kinetic energy 226 FAO data set (soil data
flags)
227 Heat capacity of soil 228 Soil diffusivity 229 Field capacity of soil 230 Vert-ly integ spec.
humidity
231 Vert-ly integ liq water cont
232 Glacier mask
129 Surface geopotential (orography)
Lampiran 3. Merubah Format data dari BIG endian menjadi LITTLE endian
Untuk melakukan proses ini terlebih dahulu disiapkan data (BIG endian) directory .../xa dan buat directory …/xalin untuk menyimpan hasil proses (LITTLE endian). Dan pastikan file
uswap
ada pada directory ~/bin Kemudian buat Script berikut dan simpan dengan nama conv_b2l.Kemudian script conv_b2l dieksekusi di konsule dengan menggunakan perintah eksekusi (./conv_b2l).
#! /bin/bash #
set -ex #
# converts bigendian data to littleendian data # compile uswap in directory uread
# INPUTDIR=/home/sofyan/remo/xa OUTPUTDIR=/home/sofyan/remo/xalin cd ${INPUTDIR} for I in * do
# for ext4,remo etc
uswap -x -i ${I} -o ${OUTPUTDIR}/${I} # for ext8
#uswap -x -d -i ${I} -o ${OUTPUTDIR}/${I} done
Lampiran 4. Script merubah rasio hutan
Script ini dibuat dengan menggunakan bahasa basic dan dijalankan dengan menggunakan system operasi Microsoft Windows. Sebelum script ini dijalankan terlebih dahulu disiapkan data ekstraksi berupa data :
o Parameter 172 : Land Sea Mask (sudah diedit : Pulau kalimantan bernilai 1 dan area lain bernilai 0)
o Parameter 174 : Albedo o Parameter 198 : Rasio Vegetasi o Parameter 200 : LAI
o Parameter 212 : Tipe Vegetasi o Parameter 229 : Kapasitas Lapang
Dim Mask(101,55) as Single ‘(Parameter 172) Land Sea Mask Dim Albd(101,55) as Single ‘(Parameter 174) Albedo
Dim RasV(101,55) as Single ‘(Parameter 198) Rasio Vegetasi Dim LAI(101,55) as Single ‘(Parameter 200) Leaf Area Index Dim TypeV(101,55) as Single ‘(Parameter 212) Tipe Vegetasi Dim KL(101,55) as Single ‘(Parameter 229) Kapasitas Lapang Dim i as Integer
Dim j as Integer
Dim TotalRasioIN as single Dim TotalRasioOUT as single
‘Modul DataProses ‘ - Membaca File Input ‘ – Membuat File Output ‘ - Membaca Data Parameter
‘ – Menulis Data Olahan (Penurunan Rasio Vegetasi) Public Sub DataProses()
‘Membaca File Input
Open “D:\Data\LSM.txt” For Input as #1 Open “D:\Data\Albedo.txt” For Input as #2 Open “D:\Data\RasioV.txt” For Input as #3 Open “D:\Data\LAI.txt” For Input as #4 Open “D:\Data\TipeV.txt” For Input as #5 Open “D:\Data\KL.txt” For Input as #6
‘Output File
Open “D:\Data\OutLSM.txt” For Output as #7 Open “D:\Data\OutAlbedo.txt” For Output as #8 Open “D:\Data\OutRasioV.txt” For Output as #9 Open “D:\Data\OutLAI.txt” For Output as #10 Open “D:\Data\OutTipeV.txt” For Output as #11 Open “D:\Data\OutKL.txt” For Output as #12
‘Membaca Data i = 0
j = 1
While not EOF(1) If i = 101 then i = 0 j = j + 1 end if i = i + 1 Input #1, Mask(i,j)
Input #2, Albd(i,j) Input #3, RasV(i,j) Input #4, LAI(i,j) Input #5, TypeV(i,j) Input #6, KL(i,j) Wend
‘ Nilai TotalRasio Awal For i = 1 to 101
For j = 1 to 55
TotalRasioIN = TotalRasioIN + (Mask(i,j)* RasV(i,j))
Next j Next i HitungUlang: Call PenurunanRasio TotalRasioOUT = 0 For i = 1 to 101 For j = 1 to 55
TotalRasioOUT = TotalRasioOUT + (Mask(i,j)* RasV(i,j))
Next j
Next i
‘ Nilai TotalRasio Bergantung dari scenario yang diinginkan If TotalRasioOUT > TotalRasioIN – (TotalRasioIN * 25/100) Then GoTo HitungUlang
End if
‘ – Menulis Data Olahan (Penurunan Rasio Vegetasi) Output #7, Mask(i,j) Output #8, Albd(i,j) Output #9, RasV(i,j) Output #10, LAI(i,j) Output #11, TypeV(i,j) Output #12, KL(i,j) ‘ Menutup File
Close #1 : Close #2 : Close #3 : Close #4 : Close #5 : Close #6 Close #7 : Close #8 : Close #9 : Close #10 : Close #11 : Close #12 End sub
‘Modul penurunan rasio hutan secara random Public sub PenurunanRasio()
Dim x as Integer Dim y as Integer
RandomUlang:
‘Menentukan Pixel yang akan dirubah i = rnd() * 101 j = rnd() * 55 x = rnd() * 101 y = rnd() * 55 If i = 0 or j = 0 or x = 0 or y = 0 _ or i = x or j = y or Mask(I,J)=0 _ or Mask(x,y) = 0 Then GoTo RandomUlang
If RasV(i,j) < RasV(x,y) then GoTo RandomUlang
‘Menukar nilai parameter pada pixel yang sudah ditentukan Mask(i,j) = Mask(x,y) Albd(i,j) = Albd(x,y) RasV(i,j) = RasV(x,y) LAI(i,j) = LAI(x,y) TypeV(i,j) = TypeV(x,y) KL(i,j) = KL(x,y) End Sub
Lampiran 5. Script untuk menjalankan model REMO
Untuk melakukan proses ini terlebih dahulu disiapkan data (LITTLE endian) pada directory xalin. Dan siapkan beberapa directory (xe,xf,xt) untuk menyimpan keluaran model Dan pastikan bahwa model iklim remo sudah ter-install pada PC yang digunakan. Kemudian buat Script berikut dan simpan dengan nama remo_ind_chain.
#!/bin/bash # set -ex cd /tmp set +e mkdir dump cd dump rm -rf * set -ex # # PFL=/home/sofyan/remo/remo5.0_pc/libs PFL2=/home/sofyan/remo/jobs EXP=400 YEXP=\'${EXP}\' # #
# Membaca jam awal dari jobs saat ini (RSA) # Membaca jam akhir dari bulan saat ini (REND) #
RSA=`cat ${PFL2}/RSA` REND=`cat ${PFL2}/REND` #
# Jika rantai selesai (mencapai REND) keluar #
if [ ${RSA} -ge ${REND} ] then
cd ${PFL2}
time put_remo_results & exit
fi #
# Akhir dari job saat ini dihitung (48 jam setelah jam awal) #
RSE=`expr ${RSA} + 48` if [ ${RSE} -ge ${REND} ] then
RSE=${REND} fi
# membuat file parameter menjalankan REMO kedalam file namanya INPUT cat > INPUT << EOF
&EMGRID PHILU=-19.0, RLALU=91.0, POLPHI=90.0, POLLAM=180.0, DLAM=0.5, DPHI=0.5, &END
&RUNCTL NHANF=$RSA, NHENDE=$RSE, YADAT='02019600', NHEAA=6, NHDEA=6, NHFORA=$RSE, NHDFOR=9999999, NHTAA=6, NHDTA=6, NHDAA=9999999, NHDDA=9999999, NHDMXN=6, DT=300.0, NHDR=6, LMOMON=.FALSE. &END &DYNCTL &END &PHYCTL HDRAD=1, LPHYEM=.FALSE., LAKKU=.FALSE., &END &NMICTL &END &PRICTL &END &DATEN YADEN='400', YRDEN='400', YEDEN='400', YFDEN='400', YTDEN='400', YADCAT='/home/sofyan/remo/xalin', YRDCAT='/home/sofyan/remo/xalin', YEDCAT='/home/sofyan/remo/xe', YFDCAT='/home/sofyan/remo/xf', YTDCAT='/home/sofyan/remo/xt',
YTVARN='APRL ','APRC ','APRS ','ALWCVI ','QVI ', 'RUNOFF ','DRAIN ','SNMEL ','DSNAC ','EVAP ','SRADS ', 'TRADS ','SRAD0 ','TRAD0 ','AHFS ','AHFL ','ACLCV ', 'WSECH ','SN ','TEMP2 ','TSECH ','TD ','TDCL ', 'TSN ','TD3 ','TD4 ','TD5 ',
&END EOF
# Disini MENJALANKAN REMO
${PFL}/remo_101x55x20x1.exe < INPUT
# Menyimpan data jam akhir job ini kedalam file, diberi nama RSA set +ex
cat > fort.20 << EOC ${RSE}
EOC
mv fort.20 ${PFL2}/RSA #
cd /home/sofyan/remo/xf
# Menghapus xf_file lama (dua file) ANZ=`ls | wc -w`
if [ ${ANZ} -eq 4 ] then
Kemudian script remo_ind_chain dieksekusi di konsule dengan menggunakan perintah eksekusi (./remo_ind_chain).
IND=1
for FILE in `ls -rt`; do if [ ${IND} -le 2 ]; then rm $FILE
fi
IND=`expr ${IND} + 1` done
fi
# kembali ke direktori awal dan menjalankan rantai berikutnya cd ${PFL2}
time remo_ind_chain &
#time remo_ind_chain >> remo_out${RSA} & #
Lampiran 6. Script untuk mengekstrak model REMO
Untuk melakukan proses ini terlebih dahulu disiapkan data keluaran model Dan pastikan bahwa model iklim remo sudah ter-install pada PC yang digunakan. ). Dan pastikan file
pure4
dan yefis ada pada directory ~/bin Kemudian buat Script berikut dan simpan dengan nama script_all.#!/bin/bash #
# This script extracting precipitation components (142+143) set -ex YY=96 MMM=01 ART='.tar' DATT=xt RUN=400 PARMA=142 PARMB=143 ERUN=e${RUN}xe NUMMERA=$PARMA,$PARMB #NUMMERA=182,139
# pindah ke direktori kerja cd /home/sofyan/remo/xeot
# buat file parameter input untuk yefis (INPUTA) dan pure4 (INPUTB) # pertama check jika kedua file ada, jika ya hapus keduanya
if [ -f INPUTA ];then rm INPUTA fi if [ -f INPUTB ];then rm INPUTB fi ################### cat > INPUTA << EOF &DATEN ICODE=${NUMMERA} IEXP=$RUN &END EOF #
cat > INPUTB << EOF 101 55 2 91 91.5 2 -19 -18.5 EOF ################### # loop tahunan
while [ ${YY} -le 96 ] do if [ ${MMM} -ne 0 ]; then MM=${MMM} MMM=0 else MM=01 MMM=0 fi
# loop bulanan
while [ ${MM} -le 01 ] do
INTE=1
# buat daftar panjang seluruh file berawal e400xt, proses satu satu for FILE in `ls ${ERUN}??${MM}${YY}??`; do
NEWFILE=`basename $FILE` echo $NEWFILE
# yefis mengubah format REMO output ke format ieee yefis < INPUTA ${FILE} ${NEWFILE}.ieee
# pure4 mengubah format ieee ke format pure binary atau grads # sekaligus dibuat ctl file untuk grads
pure4 grads ${NEWFILE}.ieee ${FILE}.grd < INPUTB >> ${FILE}.ctl
# akumulasikan seluruh file hasil ke file hasil bulanan (data 6 jam-an)
cat ${FILE}.grd >> ${ERUN}${MM}${YY}.grd rm ${FILE}
rm ${FILE}.ieee ${FILE}.grd # pindah ke indeks file berikutnya
INTE=`expr ${INTE} + 1` done
# jangan lupa juga lakukan proses serupa untuk yang jam 00 bulan berikutnya
yefis < INPUTA ${ERUN}01????00 dummy.ieee
pure4 grads dummy.ieee dummy.grd < INPUTB >> dummy.ctl cat dummy.grd >> ${ERUN}${MM}${YY}.grd
rm dummy.* ${ERUN}01????00
# mengenali nama bulan yang sedang di proses case ${MM} in 01) MON=jan;; 02) MON=feb;; 03) MON=mar;; 04) MON=apr;; 05) MON=may;; 06) MON=jun;; 07) MON=jul;; 08) MON=aug;; 09) MON=sep;; 10) MON=oct;; 11) MON=nov;; 12) MON=dec;; esac
##check jika ctl_file untuk bulan ini ada, jika tidak buatkan if [ ! -f ${ERUN}${MM}${YY}.ctl ]
then #####
cat > ${ERUN}${MM}${YY}.ctl << EOF DSET ${ERUN}${MM}${YY}.grd
UNDEF 9e+09
XDEF 101 LINEAR 91.000000 0.500000 YDEF 55 LINEAR -19.000000 0.500000
TDEF ${INTE} LINEAR 06:00Z1${MON}${YY} 06hr ZDEF 1 LINEAR 1000 -1
VARS 2
c$PARMA 1 0 CODE $PARMA c$PARMB 1 0 CODE $PARMB ENDVARS
EOF #####
Kemudian script script_all dieksekusi di konsule dengan menggunakan perintah eksekusi (./script_all).
fi
rm ${ERUN}??${MM}${YY}??.ctl # hitung bulan berikutnya
if [ ${MM} -le 08 ]; then MM=0`expr ${MM} + 1` else MM=`expr ${MM} + 1` fi done
# hitung tahun berikutnya YY=`expr ${YY} + 1` done
rm INPUTA INPUTB #######
Lampiran 7. Uji Statistik dari unsur iklim
1. CURAH HUJAN MUSIMAM
2. CURAH HUJAN KONVEKTIF Ranks 718a 728.24 522878.50 742b 732.68 543651.50 0c 1460 661d 722.40 477505.50 799e 737.20 589024.50 0f 1460 678g 733.85 497548.50 781h 726.66 567521.50 1i 1460 Negative Ranks Positive Ranks Ties Total Negative Ranks Positive Ranks Ties Total Negative Ranks Positive Ranks Ties Total R-25 - KONTROL R-50 - KONTROL R-50 - R-25
N Mean Rank Sum of Ranks
R-25 < KONTROL a. R-25 > KONTROL b. KONTROL = R-25 c. R-50 < KONTROL d. R-50 > KONTROL e. KONTROL = R-50 f. R-50 < R-25 g. R-50 > R-25 h. R-25 = R-50 i. Test Statisticsb -.645a -3.461a -2.174a .519 .001 .030 Z
Asymp. Sig. (2-tailed)
R25 -KONTROL
R50
-KONTROL R-50 - R-25 Based on negative ranks.
a.
Wilcoxon Signed Ranks Test b. Ranks 570a 693.61 395359.00 887b 751.74 666794.00 3c 1460 562d 689.43 387458.50 897e 755.42 677611.50 1f 1460 728g 731.55 532570.00 732h 729.45 533960.00 0i 1460 Negative Ranks Positive Ranks Ties Total Negative Ranks Positive Ranks Ties Total Negative Ranks Positive Ranks Ties Total R 25 - Kontrol R 50 - Kontrol R 50 - R 25
N Mean Rank Sum of Ranks
R 25 < Kontrol a. R 25 > Kontrol b. Kontrol = R 25 c. R 50 < Kontrol d. R 50 > Kontrol e. Kontrol = R 50 f. R 50 < R 25 g. R 50 > R 25 h. R 25 = R 50 i. Test Statisticsb -8.449a -9.013a -.043a .000 .000 .966 Z
Asymp. Sig. (2-tailed)
R 25 - Kontrol R 50 - Kontrol R 50 - R 25 Based on negative ranks.
a.
Wilcoxon Signed Ranks Test b.
3. EVAPORASI 4. LIMPASAN Ranks 486a 799.77 388687.50 973b 695.15 676382.50 1c 1460 583d 831.97 485037.50 876e 662.14 580032.50 1f 1460 879g 745.71 655480.50 580h 706.19 409589.50 1i 1460 Negative Ranks Positive Ranks Ties Total Negative Ranks Positive Ranks Ties Total Negative Ranks Positive Ranks Ties Total R 25 - Kontrol R 50 - Kontrol R 50 - R 25
N Mean Rank Sum of Ranks
R 25 < Kontrol a. R 25 > Kontrol b. Kontrol = R 25 c. R 50 < Kontrol d. R 50 > Kontrol e. Kontrol = R 50 f. R 50 < R 25 g. R 50 > R 25 h. R 25 = R 50 i. Test Statisticsc -8.937a -2.951a -7.638b .000 .003 .000 Z
Asymp. Sig. (2-tailed)
R 25 - Kontrol R 50 - Kontrol R 50 - R 25 Based on negative ranks.
a.
Based on positive ranks. b.
Wilcoxon Signed Ranks Test c. Ranks 472a 582.10 274753.50 752b 631.58 474946.50 236c 1460 551d 700.47 385959.00 908e 747.92 679111.00 1f 1460 709g 720.10 510549.00 750h 739.36 554521.00 1i 1460 Negative Ranks Positive Ranks Ties Total Negative Ranks Positive Ranks Ties Total Negative Ranks Positive Ranks Ties Total R25 - Kontrol R50 - Kontrol R50 - R25
N Mean Rank Sum of Ranks
R25 < Kontrol a. R25 > Kontrol b. Kontrol = R25 c. R50 < Kontrol d. R50 > Kontrol e. Kontrol = R50 f. R50 < R25 g. R50 > R25 h. R25 = R50 i. Test Statisticsb -8.092a -9.106a -1.366a .000 .000 .172 Z
Asymp. Sig. (2-tailed)
R25 - Kontrol R50 - Kontrol R50 - R25 Based on negative ranks.
a.
Wilcoxon Signed Ranks Test b.
5. SUHU UDARA Ranks 404a 707.98 286023.50 1052b 736.38 774672.50 4c 1460 289d 693.15 200319.00 1171e 739.72 866211.00 0f 1460 513g 635.50 326010.00 945h 780.53 737601.00 2i 1460 Negative Ranks Positive Ranks Ties Total Negative Ranks Positive Ranks Ties Total Negative Ranks Positive Ranks Ties Total R25 - Kontrol R50 - Kontrol R50 - R25
N Mean Rank Sum of Ranks
R25 < Kontrol a. R25 > Kontrol b. Kontrol = R25 c. R50 < Kontrol d. R50 > Kontrol e. Kontrol = R50 f. R50 < R25 g. R50 > R25 h. R25 = R50 i. Test Statisticsb -15.226a -20.664a -12.799a .000 .000 .000 Z
Asymp. Sig. (2-tailed)
R25 - Kontrol R50 - Kontrol R50 - R25 Based on negative ranks.
a.
Wilcoxon Signed Ranks Test b.
Lampiran 8. Data curah hujan di beberapa stasiun di pulau Kalimantan pada tahun 1996
No. Nama Stasiun Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total
1 Singkawang 465 350 214 231 112 340 191 327 282 525 263 277 3577 2 Pontianak 249 363 344 354 159 330 204 340 183 639 276 235 3676 3 Anjungan 294 357 320 335 153 330 204 338 207 611 278 244 3671 4 Kembayan 245 273 319 229 73 359 202 290 266 525 443 351 3575 5 Paloh 563 355 156 148 78 351 171 320 308 488 242 295 3475 6 Siantan 345 353 289 308 143 78 351 171 320 308 488 242 3396 7 Susilo Sintang 438 321 362 382 166 335 315 350 432 485 306 276 4168 8 Nanga Pinoh 423 328 354 363 166 331 345 351 403 477 313 278 4132 9 Banjarmasin 415 339 350 277 220 249 196 440 113 358 324 491 3772 10 Banjarbaru 414 340 349 277 220 250 197 439 114 357 324 489 3770 11 Stajer 336 326 253 321 181 440 607 373 238 308 130 280 3793 12 Palangkaraya 256 405 240 186 165 266 171 366 246 189 391 244 3125 13 Muara Tewah 256 386 419 230 249 293 111 378 218 341 568 309 3758 14 Balikpapan - 392 - - - 287 192 - - - 15 Tarakan 304 275 448 439 330 339 248 402 442 - 378 579 4184 16 Samarinda 315 385 300 203 304 278 129 319 209 304 329 332 3407 17 Tanjung Redup 372 377 376 278 - - 195 211 - - 224 - - 18 Tanjung Selor 323 304 366 232 262 316 183 284 107 294 251 275 3197 19 Long Bawang 292 113 183 262 216 239 - - -
